At Freddie Mac, our mission of Making Home Possible is what motivates us, and it’s at the core of everything we do. Since our charter in 1970, we have made home possible for more than 90 million families across the country. Join an organization where your work contributes to a greater purpose.

Position Overview:

Are you looking for an opportunity to put your technical skills to use working on advanced modeling challenges?

Freddie Mac’s Single-Family Modeling team is currently seeking a Quantitative Analytics Senior who will be responsible for the development and execution of quantitative analytics and statistical models to support business and risk management decisions as a member of the Model Strategy team. Apply now and learn why there’s #MoreAtFreddieMac!

Our Impact:

Our team is responsible for accelerating the development of models across the Single Family Modeling department especially in the context of credit risk and credit summarization.

Your Impact:

• Developing analytical methods and models that assess the credit risk of new and existing financial and mortgage products. Conducting research on enhancements to existing models and applying industry best practices and techniques to meet various business needs.

• Providing innovative, detailed and practical solutions to an extensive range of demanding and complicated problems.

• Implementing statistical models in efficient software languages, coding model prototypes for specification and test cases, modifying source codes in the existing application.

• Coordinating the testing through the model implementation, conducting back tests to monitor the model performance, and performing economic tests and stress tests to validate the model forecast results.

• Providing modeling and analytical support to a line of business or product area, functioning as day-to-day technical specialist.

• Preparing documentation for the technical analytics and rationale through the model development to comply with model oversight and support model review for approval.

• Working under limited direction, independently resolving and developing approach to solutions.

Qualifications:

• Doctorate degree (or Master’s degree with equivalent work experience) in quantitative finance, statistics, economics or a related quantitative field

• Coursework or work experience applying predictive modeling techniques from finance, statistics, mathematics, data science, and computer programming to large data sets.  Qualifying coursework may include--but is not limited to—statistics, mathematical programming, optimization, machine learning, computational methods, design and analysis of algorithms, Bayesian methods, derivatives, and Monte Carlo methods

• Coursework or work experience writing statistical or optimization programs to develop models and algorithms. Programming languages may include--but are not limited to—SAS, Python, R, SQL, Java, and MATLAB

• Proficiency in programming languages such as SAS, Python, SQL or Unix

• Theoretical understanding of popular data science methods and auxiliary analytics, including machine learning modeling methods, explainability, uncertainty measurement, etc.

• Experience delivering solutions to complex and ambiguous business problems in a fast-paced environment.

• Experience working with large data sets and relational databases. Knowledge of consumer credit report data is preferred.

• Experience in statistical model development and implementation is preferred

Keys to Success in this Role:

• Exceptional quantitative and analytics skills

• Strong knowledge of statistical models, tools and techniques

• Strong programming skills

• Rigorous attention to detail

• Ability to learn and interpret new concepts quickly

• Effective interpersonal skills including the ability to collaborate successfully with other team members.

• Strong oral and written communication skills with ability to succinctly describe complex technical scenarios in simple, easily-understood terms.

Current Freddie Mac employees please apply through the internal career site.

We consider all applicants for all positions without regard to gender, race, color, religion, national origin, age, marital status, veteran status, sexual orientation, gender identity/expression, physical and mental disability, pregnancy, ethnicity, genetic information or any other protected categories under applicable federal, state or local laws. We will ensure that individuals are provided reasonable accommodatio